[Solved] How to cluster with K-means, when number of clusters and their sizes are known [closed]

Question

It won’t be k-means anymore.

K-means is variance minimization, and it seems your objective is to produce paritions of a predefined size, not of minimum variance.

However, here is a tutorial that shows how to modify k-means to produce clusters of the same size. You can easily extend this to produce clusters of the desired sizes instead of the average size. It’s fairly easy to modify k-means this way. But the results will be even more meaningless than k-means results on most data sets. K-means is often just as good as random convex partitions.

Accepted Answer

It won’t be k-means anymore.

K-means is variance minimization, and it seems your objective is to produce paritions of a predefined size, not of minimum variance.

However, here is a tutorial that shows how to modify k-means to produce clusters of the same size. You can easily extend this to produce clusters of the desired sizes instead of the average size. It’s fairly easy to modify k-means this way. But the results will be even more meaningless than k-means results on most data sets. K-means is often just as good as random convex partitions.